Identification of electroporation sites in the complex lipid organization of the plasma membrane

  1. Lea Rems  Is a corresponding author
  2. Xinru Tang
  3. Fangwei Zhao
  4. Sergio Pérez-Conesa
  5. Ilaria Testa
  6. Lucie Delemotte  Is a corresponding author
  1. KTH Royal Institute of Technology, Dept. Applied Physics, Science for Life Laboratory, Sweden
  2. University of Ljubljana, Faculty of Electrical Engineering, Slovenia
  3. University of Chinese Academy of Sciences, China
7 figures, 3 tables and 2 additional files

Figures

Figure 1 with 1 supplement
The average mammalian and human brain plasma membranes.

Two equilibrated membranes taken from the study of Ingólfsson et al., 2017 were cut into four 30 nm x 30 nm large pieces each. The pie charts show the fraction of lipid subgroups in the inner and outer leaflets of the membranes. CHL, cholesterol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; GM, gangliosides; PS, phosphatidylserine; FS, fully saturated lipids; MU, monounsaturated lipids; PU, polyunsaturated lipids. Figure inspired by Ingólfsson et al., 2017.

Figure 1—figure supplement 1
Fraction of different lipidsin the inner and outer membrane leaflets.

Lipids are separated based on both their headgroup architecture and tail saturation. Cholesterol is not included in the graphs. Graphs on the left and right, respectively, correspond to average plasma membrane (APM) and brain plasma membrane (BPM).

Location and kinetics of pore formation in each membrane upon depolarization and hyperpolarization.

The pore locations are expressed relative to the dimensions of the simulation box at the poration time, to correct for the natural fluctuations in the membrane area during the simulation. All data points correspond to the first poration event. The colour of the circle codes for poration time according to the colour bar. (A) Average plasma membranes. (B) Brain plasma membranes.

Figure 3 with 2 supplements
Membrane features that favour and disfavour poration.

(A) (Left) Definition of porated and non-porated locations in one of the membranes; (middle) most pore locations colocalize with increased polyunsaturated lipids density; (right) the corresponding histogram of polyunsaturated lipids density values in porated and non-porated locations. (B) Distances (KL divergence) between probability density estimates of individual features in porated and non-porated locations. The higher the bar, the more the feature influences poration. Positive and negative bars show whether a feature favours or disfavours poration, respectively. APL, area per lipid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; GM, gangliosides; PS, phosphatidylserine; PI, phosphatidylinositol; PA, phosphatic acid; PIP, phosphatidylinositol phosphate; LPC, lysolipids; CE, ceramides; DAG, diglycerol; CHL, cholesterol; FS, fully saturated lipids; MU, monounsaturated lipids; PU, polyunsaturated lipids.

Figure 3—figure supplement 1
Membrane surface fitting with MemSurfer.

(A) Points corresponding the lipid headgroups. (B) Delaunay triangulations. (C, D) Surface smoothed through a Poisson reconstruction approach. (E) Top view of the triangulated surface. (F) Determination of the area per lipid (shaded part).

Figure 3—figure supplement 2
Kullback–Leibler divergence.

Distances between probability density estimates (KL divergence) of individual features in porated and non-porated locations, calculated according to Equation 7. Graphs are shown separately for the outer and inner leaflet and for the mean of both leaflets, see the titles above each graph. The higher the bar, the more the feature influences poration. Positive and negative bars show whether a feature favours or disfavours poration, respectively. The graphs on the left side correspond to values extracted from a 10-ns-long trajectory before electric field application. The graphs on the right side correspond to values extracted from a 10-ns-long trajectory, where a non-porating electric field of +106.3 mV/nm (analysis for depolarization) or −106.3 mV/nm (analysis for hyperpolarization) was applied.

Random forest output.

(A) Comparison between the predicted poration sites (red dots) and real poration sites (yellow dots). Only locations that were classified as porated in >90% of the trajectory frames are shown. (B) Feature importance score for average plasma membrane (APM) and brain plasma membrane (BPM) obtained under hyperpolarization and depolarization. The feature importance score quantifies how much each feature is used in each tree of the random forest.

Figure 5 with 6 supplements
Kinetics of membrane poration.

(A) Probability distribution of the first poration times, sampled from 60 simulations, and approximated with kernel density estimation. (B) The time course of λ0(t). (C) Credible intervals of exp(βi) for average plasma membrane (APM) and brain plasma membrane (BPM) under depolarization and hyperpolarization. (D, E) Correlation between βi and the steady-state relative change in membrane thickness Δd/d0 and projected area ΔA/A0 normalized by E2, extracted from least-square fits presented in Figure 5—figure supplements 5 and 6. Square symbols show the average over all four membranes, whereas the error bars show the range of values in four membranes. The ratio ΔA/A0 / E2 is inversely proportional to the area compressibility (or stretching) modulus (Needham and Hochmuth, 1989).

Figure 5—figure supplement 1
Posterior predictive checks of the Bayesian inference procedures.

Graphs show the cumulative distribution function of poration times, grouped from all membranes together. We compare the observed data (blue), the observed data with the time discretized in order to be able to use stepwise defined functions in the model (orange), and the data that the model is able to generate (green). The agreement between the input data and the data generated by our modelling is satisfactory. The presented posterior predictive checks are done for (A) combining the different membrane patches of the same membrane type and polarity in the same group; (B) having each membrane fragment as a different group; and (C) for the time-dependent β model.

Figure 5—figure supplement 2
The time-dependent βi(t) model.

(A) Time evolution of λ0(t). (B) Time evolution of βi(t). Note that the value of βi(t) is slightly different from the ones obtained in the time-independent model in Figure 5. Since all coefficients are parametrized simultaneously with all the data, different combinations of magnitudes of λ0(t) and βi can result in an equivalent λi(t). Therefore, in order to compare how rates increase or decrease in two different systems i and j, only the relative change can be studied λi(t)/λj(t) = exp(βi)/exp(βj).

Figure 5—figure supplement 3
Credible intervals of exp(βi) separately for each of the eight membranes considered and both electric field polarities.
Figure 5—figure supplement 4
Upon exposure to electric field, the membranes thin and increase their area.

The graphs show the time courses of the relative changes in average thickness Δd/d0 and projected area ΔA/A0 in one of the average plasma membrane (APM) membranes (first row) and one of the brain plasma membrane (BPM) membranes (second row) when applying non-porating electric fields E of 0, 42.5, 63.8, 85.1, or 106.3 mV/nm. Each curve shows mean ± standard deviation (SD) of 5 simulation runs. The average membrane thickness was obtained by averaging the thickness values computed at all lipid positions by MemSurfer.

Figure 5—figure supplement 5
Relative change in membrane thickness under non-porating electric fields.

The relationship between the steady-state relative change in the average membrane thickness Δd/d0 and E2, determined from Figure 5—figure supplement 4. Each graph corresponds to one of eight simulated membranes; hyp, hyperpolarization; dep, depolarization.

Figure 5—figure supplement 6
Relative change in the projected membrane area under non-porating electric fields.

The relationship between the steady-state relative change in the membrane area ΔA/A0 and E2. Graphs were obtained in the same way as for the relative change in membrane thickness, presented in Figure 5—figure supplement 5. The relative increase in the membrane area in steady state is linearly proportional to E2, as expected for Maxwell stress.

Author response image 1
Correlation between membrane thickness and the free energy barrier for creating a 3-nm-radius pore in a lipid bilayer.

Each dot corresponds to one of 18 monocomponent coarse-grained Martini membranes, made from fully saturated and monounsatured lipids with different chain lengths and different headgroups (PC, PE, PS, and PG). Reproduced from data tabulated in (Hu et al., 2015).

Author response image 2
Time course of the induced transmembrane voltage, pore density, and membrane conductivity at the cell pole (q = 0) upon exposure to 1-ms-long electric pulse.

Only the first 10 ms after the onset of the electric pulse are shown for clarity. The results are reproduced from the paper by DeBruin and Krassowska 1999, doi: 10.1016/S0006-3495(99)76973-0.

Tables

Table 1
Separation of lipids into groups based on their headgroup type or tail saturation.

The lipids are grouped into subtypes, depending on their headgroup type and tail saturation. Lipids are considered to be fully saturated, if they contain no double bonds in any of the tails. Lipids are considered to be monounsaturated, if they contain exactly one double bond in one or both of the tails. Lipids are considered to be polyunsaturated, if they contain at least two double bonds in one or both of the tails*.

Group nameAbbrev.Martini lipids
PhosphatidylcholinesPCDAPC, DOPC, DPPC, OIPC, OUPC, PAPC, PEPC, PFPC, PIPC, POPC, PUPC
PhosphatidylethanolaminesPEDAPE, DOPE, DUPE, OAPE, OIPE, OUPE, PAPE, PIPE, POPE, PQPE, PUPE
SphingomyelinsSMBNSM, DBSM, DPSM, DXSM, PBSM, PGSM, PNSM, POSM, XNSM
GangliosidesGMDBG1, DPG1, DXG1, PNG1, POG1, XNG1, DBG3, DPG3, DXG3, PNG3, POG3, XNG3, DBGS, DPGS, PNGS, POGS
CeramidesCEDBCE, DPCE, DXCE, PNCE, POCE, XNCE
LysolipidsLPCAPC, IPC, OPC, PPC, UPC, IPE, PPE
DiglyceridesDAGPODG, PIDG, PADG, PUDG
PhosphatidylserinesPSDAPS, DOPS, DPPS, DUPS, OUPS, PAPS, PIPS, POPS, PQPS, PUPS
PhosphatidylinositolsPIPOPI, PIPI, PAPI, PUPI
Phosphatic acidsPAPOPA, PIPA, PAPA, PUPA
Phosphatidylinositol phosphatesPIPPAP1, PAP2, PAP3, POP1, POP2, POP3
CholesterolCHOLCHOL
Fully saturated tailsFSDPPC, DBSM, DPSM, DXSM, PBSM, DPPS, DBCE, DPCE, DXCE, PPC, PPE, DBG1, DPG1, DXG1, DBG3, DPG3, DXG3, DBGS, DPGS
Monounsaturated tailsMUDOPC, POPC, DOPE, POPE, BNSM, PGSM, PNSM, POSM, XNSM, DOPS, POPS, POPI, POP1, POP2, POP3, POPA, PODG, PNCE, POCE, XNCE, OPC, PNG1, POG1, XNG1, PNG3, POG3, XNG3, PNGS, POGS
Polyunsaturated tailsPUOIPC, OUPC, PAPC, PEPC, PFPC, PIPC, PUPC, OAPE, OIPE, OUPE, PAPE, PIPE, PQPE, PUPE, OUPS, PAPS, PIPS, PQPS, PUPS, PAPI, PIPI, PUPI, PAP1, PAP2, PAP3, PAPA, PIPA, PUPA, PADG, PIDG, PUDG, APC, IPC, UPC, IPE, DAPC, DUPE, DAPE, DAPS, DUPS
  1. *

    The way in which lipids are grouped by tail saturation is different than in Ingólfsson et al., 2017, where the grouping is based on the total number of double bonds in both lipid tails. Here, the grouping is motivated by the role of lipid oxidation in electroporation, whereby lipid tails containing two or more double bonds are considerably more prone to oxidative damage than tails containing a single double bond. This is because bis-allylic hydrogens are much more easily abstracted by free radicals compared to allylic hydrogens (Reis and Spickett, 2012). Furthermore, membranes made of polyunsaturated lipids (by our definition) were found to be considerably more prone to poration/rupture by mechanical stretching compared to membranes made of lipids containing a single bond in one or both lipid tails (Olbrich et al., 2000). Thus, we consider that a lipid is polyunsaturated only if it contains at least one polyunsaturated tail.

Table 2
The total number of lipids and the percentage of individual lipid groups in each of the eight membranes.

The numbers correspond to inner/outer leaflet.

Average plasma membraneBrain plasma membrane
mem #1mem #2mem #3mem #4mem #1mem #2mem #3mem #4
Total number of lipids in each leaflet
in / out1503/16381564/16631571/16501553/16631728/18911740/18471770/18861764/1906
Percentage of lipid groups
CHOL27.7/31.730.4/32.130.8/32.129.6/33.044.5/45.745.1/46.145.4/46.344.4/46.4
PC17.9/36.418.9/34.315.9/34.717.8/34.312.4/23.012.4/23.614.8/25.814.2/21.9
PE24.5/6.621.9/4.624.8/5.325.2/6.222.8/9.721.8/11.719.6/9.220.9/10.5
SM10.6/18.78.9/19.59.7/19.68.6/19.12.1/8.92.1/9.02.3/9.22.8/8.5
GM0.0/3.70.0/7.00.0/5.20.0/4.70.0/11.30.0/8.60.0/8.00.0/11.5
PS11.3/0.010.7/0.09.6/0.010.6/0.09.8/0.011.0/0.09.1/0.08.7/0.0
PI3.7/0.04.5/0.05.0/0.05.0/0.05.4/0.04.9/0.05.0/0.05.1/0.0
PIP2.0/0.01.7/0.01.1/0.01.5/0.01.4/0.00.9/0.01.3/0.01.7/0.0
PA1.6/0.02.1/0.02.0/0.00.9/0.00.3/0.00.6/0.00.5/0.00.2/0.0
LPC0.0/1.50.0/0.80.0/1.40.0/1.00.5/0.30.2/0.50.5/0.60.3/0.3
DAG0.5/1.00.3/0.50.4/0.80.3/1.00.4/0.20.3/0.10.8/0.31.0/0.1
CE0.3/0.40.6/1.10.6/0.90.6/0.70.2/0.70.6/0.30.7/0.50.7/0.7
FS6.6/12.24.4/14.85.3/14.74.8/14.05.1/22.45.2/19.36.1/19.85.8/20.6
MU24.6/26.725.4/29.524.4/27.125.4/26.613.2/15.512.9/15.914.1/16.615.2/17.2
PU41.1/29.439.8/23.639.5/26.240.2/26.537.2/16.336.8/18.734.4/17.234.5/15.8
Table 3
Prediction accuracy by machine learning models, reported for the training and test datasets; dep, depolarization; hyp, hyperpolarization.
#Training datasetTest datasetRandom forest all featuresSVM all featuresNeural network all featuresRandom forestfour features
TrainTestTrainTestTrainTestTrainTest
Same membrane composition, same E-field polarity; train on dataset from two out of four membranes
1APM-dep, mem 1 and 2APM-dep, mem 3 and 4100%83%97%83%97%81%100%82%
2APM-dep, mem 1 and 3APM-dep, mem 2 and 4100%80%96%80%94%77%100%76%
3APM-dep, mem 1 and 4APM-dep, mem 2 and 3100%85%96%85%91%81%100%82%
4BPM-dep, mem 1 and 2BPM-dep, mem 3 and 4100%87%97%86%94%86%100%84%
5BPM-dep, mem 1 and 3BPM-dep, mem 2 and 4100%82%97%84%97%79%100%85%
6BPM-dep, mem 1 and 4BPM-dep, mem 2 and 3100%75%97%83%96%77%100%82%
Different membrane composition, same E-field polarity
7APM-depBPM-dep100%83%94%82%93%83%100%83%
8APM-hypBPM-hyp100%83%94%82%95%83%100%83%
Same membrane composition, different E-field polarity
9APM-depAPM-hyp100%86%95%83%94%81%100%84%
10BPM-depBPM-hyp100%90%95%86%95%84%100%88%
11APM and BPM-depAPM and BPM-hyp100%88%94%84%93%82%100%86%
Same membrane composition, same E-field polarity; train on 60% dataset from all four membranes
12APM-dep (60%)APM-dep (40%)100%99%94%91%91%91%100%92%
13APM-hyp (60%)APM-hyp (40%)100%99%94%90%92%92%100%91%
14BPM-dep (60%)BPM-dep (40%)100%99%95%93%94%94%100%93%
15BPM-hyp (60%)BPM-hyp (40%)100%99%94%91%95%94%100%93%
16APM and BPM-dep (60%)APM and BPM-dep (40%)100%99%94%92%92%92%100%92%
17APM and BPM-hyp (60%)APM and BPM-hyp (40%)100%99%92%92%91%91%100%92%

Additional files

Transparent reporting form
https://cdn.elifesciences.org/articles/74773/elife-74773-transrepform1-v2.docx
Supplementary file 1

Histograms of tested features.

Histograms showing the values of tested features in non-porated (empty bars) and porated (blue bars) locations, for average plasma membrane (APM) and for brain plasma membrane (BPM). The purple and yellow solid lines show kernel smoothing density estimates. The title above each group of graphs indicates the dataset for which the histograms are shown.

https://cdn.elifesciences.org/articles/74773/elife-74773-supp1-v2.pdf

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  1. Lea Rems
  2. Xinru Tang
  3. Fangwei Zhao
  4. Sergio Pérez-Conesa
  5. Ilaria Testa
  6. Lucie Delemotte
(2022)
Identification of electroporation sites in the complex lipid organization of the plasma membrane
eLife 11:e74773.
https://doi.org/10.7554/eLife.74773